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MASTER OF SCIENCE THESIS Analysis and preliminary characterization of a MEMS cantilever-type chemical sensor by Daniel Arecco NEST – NanoEngineering, Science, and Technology CHSLT – Center for Holographic Studies and Laser micro-mechaTronics Mechanical Engineering Department Worcester Polytechnic Institute Worcester, MA 01609-2280 17 December 2003

MEMS Cantilever

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MASTER OF SCIENCE THESIS Analysis and preliminary characterization of a MEMS cantilever-type chemical sensor by Daniel Arecco NEST NanoEngineering, Science, and Technology CHSLT Center for Holographic Studies and Laser micro-mechaTronics Mechanical Engineering Department Worcester Polytechnic Institute Worcester, MA 01609-2280 17 December 2003 3 Copyright 2003 by NEST NanoEngineering, Science, and Technology CHSLT Center for Holographic Studies and Laser micro-mechaTronics Mechanical Engineering Department Worcester Polytechnic Institute Worcester, MA 01609-2280 All rights reserved 4SUMMARY This Thesis relates to the continually advancing field of microelectromechanical systems (MEMS). With MEMS technology, there are many different areas of concentration available for research. This Thesis addresses analysis and preliminary characterization of a cantilever-type MEMS chemical sensor for detection of chemicals and organic components operating at room temperature (20C and sea level pressure of 1 atm). Such sensors can be useful in a wide variety of applications. There currently exist several different types of MEMS chemical sensors. Each is based on a different detection method, e.g., capacitive, thermal, resistive, etc., and is used for specific tasks. Out of all currently available detection methods, the most common is the gravimetric method. The gravimetric sensor works by absorbing the chemical in a special material, usually a polymer, which alters the overall mass of the sensing element that can then be measured, or detected, to identify the chemical absorbed. One of the more exciting developments in the field of gravimetric chemical MEMS has been with the advancement of cantilever-type sensors. These cantilevers are small and usually on the order of only about 300 m in length. In order to utilize the gravimetric method, a cantilever is coated with a polymer that allows an analyte to bond to it and change its mass, which in turn changes the resonant frequency of the cantilever. The change in frequency can then be measured and analyzed and from it, the amount of absorbed mass can be calculated. Current research in the cantilever-type resonating sensors for the detection of hydrogen is developing measurement capabilities of 1 ppm (part-per-million). 5In this Thesis number of sample cantilevers were qualitatively assessed and their dimensional geometry measured. Based on these measurements, frequency data were obtained. In addition, the overall uncertainty in the resonant frequency results was calculated and the contributing factors to this uncertainty were investigated. Experimental methods that include laser vibrometry, optoelectronic laser interferometric microscopy (OELIM), and atomic force microscopy (AFM), were utilized to measure the frequency responses of the samples. The analytically predicted natural frequencies were compared to the experimental data to determine correlation subject to the uncertainty analysis. Parametric analyses involving chemical absorption processes were also conducted. Such analyses considered different parameters, e.g., damping and stiffness as well as changes in their values, to determine contributions they make to the quality of the frequency data and the effect they have on sensitivity of the MEMS cantilever-type chemical sensors. Once these parametric analyses were completed, it was possible to estimate the sensitivity of the cantilever, or the ability for the cantilever to detect frequency shifts due to absorption of the target chemical. Results of the parametric analyses of the fundamental resonant frequency were then correlated with the sensitivity results based on the chemical absorption. This Thesis correlates many results and ideas and probes problems revolving around the analysis and characterization of a MEMS cantilever-type chemical sensor. 6ACKNOWLEDGEMENTS I would first like to thank my parents, Carlos Arecco M.D. and Vera Arecco Ph.D. for everything that they have provided for me in my life. Without their undying love and support, I would not be where I am today. First off, I have to thank Professor Ryszard J. Pryputniewicz for the wonderful opportunity he has given me to work in a research field that has challenged me to new limits. He has taught me things beyond the call of a typical advisor. Thank you. Secondly, I have to send a special thank you to Mr. (soon to be Dr.) Shivananda Pai Mizar for all the invaluable support that he showed whenever I was in need and for his affirming to my getting done. May your teaching career be rewarding. Another special thank must go to Mr. Peter Hefti from Switzerland, who lent me his wealth of technical knowledge and support for completing my experimental work and also for his stories that would always boost morale. May they never catch you. I would also like to thank Professor Cosme Furlong for his support in my computational work and all the random questions I had. I would also like to thank everyone else at the Center for Holographic Studies and Laser micro-mechaTroncis (CHSLT): Kevin Bruff, Wei Han, Adam Klempner, Ronald Kok, Ryan Marinis, and Vicky Steward. I would also like to acknowledge all the people that helped me along the way in there own ways: Barbara Edilberti, Janice Dresser, and Todd Billings. Finally, I would like to thank my brother, Andres, because I can. This M.S. Thesis will stay with me for the rest of my life and I am glad to have accomplished it with all the great people of Worcester Polytechnic Institute. 7TABLE OF CONTENTS Copyright 3 Summary 4 Acknowledgements 6 Table of contents 7 List of figures 11 List of tables 16 Nomenclature 18 1. Objectives 21 2. Introduction 22 2.1. Micromachine beginnings 22 2.2. MEMS Foreword 24 2.3. Sensors 27 2.3.1. Mechanical detection 29 2.3.2. Optical detection 31 2.3.3. Electrical detection 32 2.3.4. Chemical detection 32 2.4. MEMS cantilever chemical sensor 35 2.4.1. Material 35 2.4.2. Fabrication 37 2.4.3. Packaging 39 2.4.4. Sensing types and methods 40 2.4.5. Resonance frequency 42 2.4.6. Cantilever actuation methods 43 2.4.7. Cantilever I/O 44 2.4.8. Polymer thickness 46 2.4.10. Ficks law of diffusion 47 2.4.11. Reference cantilevers 47 2.4.12. Quality factor 48 2.4.13. Electrical aspects 49 Wheatstone bridge 50 Feed back Loop 50 2.4.14. Inherent Problems 51 Residual Stresses 51 Stiction 53 8 2.4.15. Effects of temperature 54 2.4.16. Effects of pressure 54 3. MEMS samples 56 3.1. MikroMasch USA cantilevers 56 3.2. Chemicals used 58 3.2.1. Palladium 59 3.2.2. Hydrogen 60 3.2.3. Titanium 60 3.3. Coating process 61 3.3.1. Complete versus partial coating 62 3.3.2. Effects of humidity 63 3.3.3. Time limits 63 4. Methodology 65 4.1. ACES methodology 65 4.2. Analytical investigations 67 4.2.1. Sensitivity 68 4.2.2. Equivalent variables 71 4.2.3. Gas concentration 71 4.2.4. Absorption 72 Diffusion rate 75 Volume increase 78 Change in modulus of elasticity 79 4.2.5. Resonant frequency and frequency shift 81 4.2.6. Spring constant consideration 83 4.2.7. Damping coefficient consideration 85 4.2.8. Free vibration system 87 4.2.9. Changing stiffness in a free vibration system 89 4.2.10. Harmonically excited system 89 4.2.11. Uncertainty analysis 92 4.3. Computational analysis 93 4.4. Experimental solutions 94 4.4.1. Instrument calibration 95 4.4.2. Geometrical characterization of the cantilevers 95 4.4.3. SEM scanning election microscope 96 4.4.4. Determination of quality factor and damping coefficient 97 4.4.5. Optoelectronic methodology 98 4.4.6. Determination of resonant frequency by AFM 104 4.4.7. Laser vibrometry 105 4.4.8. Test chamber 107 4.5. Data analysis 109 95. Results 111 5.1. Microscope and SEM data 111 5.2. Analytical results 116 5.3. Optoelectronic measurements 121 5.4. AFM results 123 5.5. Computational results 124 5.6. Comparison of results 127 5.7. Parameters of the coated cantilevers 129 5.8. Sensitivity solutions 130 5.9. Absorption 133 5.10. Determination of the damping coefficient 136 5.11. Complete cantilever analysis 139 5.11.1. Case 1: changing mass 140 5.11.2. Case 2: changing mass and stiffness 141 5.11.3. Case 3: changing mass with damping 142 5.11.4. Case 4: changing mass and stiffness with damping 142 5.11.5. Design equation selection 143 5.11.6. Lower limit frequency shift 144 5.11.7. Optimization of the cantilever response 146 6. Conclusions 148 7. References 151 Appendix A. Material properties 161 Appendix B. Analytical calculations 164 B.1. Fundamental resonance frequency 164 B.2. Spring constant 165 B.3. Dynamic mass 168 B.3.1. Dynamic mass constant 170 B.4. Sensitivity calculations of the cantilever 171 B.4.1. Sensitivity of an end-loaded cantilever 171 B.4.2. Sensitivity of a completely coated cantilever 173 B.5. Frequency shift for a free, undamped vibration 174 B.6. Frequency shift for a free, undamped vibration with a changing stiffness 176 B.7. Free vibration with viscous damping 177 A.7.1. Frequency shift for a free, damped vibration 178 A.7.2. Frequency shift for a free, damped vibration with a changing stiffness 180 A.8. Harmonically excited system with viscous damping 181 10Appendix C. Uncertainty analysis 183 C.1. Uncertainty in fundamental resonant frequency 183 C.1.1. Uncertainty calculations for the 350 m long cantilever 183 C.1.2. Uncertainty calculations for the 300 m long cantilever 187 C.1.3. Uncertainty calculations for the 250 m long cantilever 189 C.1.4. Average overall percentage uncertainty in frequency 190 C.2. Frequency uncertainty in a palladium coated cantilever 191 C.2.1. Uncertainty in frequency for a free vibrations system 191 C.2.2. Uncertainty in frequency for a free vibrations system with a changing stiffness 199 C.2.3. Uncertainty in frequency for a free vibrations system with damping 205 C.2.4. Uncertainty in frequency for a damped, changing stiffness vibrations system 210 Appendix D. Test chamber 215 Appendix E. Analytical frequency data 218 11LIST OF FIGURES Fig. 2.1. A dust mite dwarfs a MEMS device (SNL, 2001) 23 Fig. 2.2. A human is compared to a MEMS device (SNL, 2001) 23 Fig. 2.3. A microchain device (SNL, 2001) 25 Fig. 2.4. Details of a DMD chip (TI, 2003) 26 Fig. 2.5. A schematic illustration of MEMS components and their interdependence (Madou, 2002) 28 Fig. 2.6. A view of an entire dual-axis accelerometer where the proof mass can be seen with all the perforations in the middle and a quarter-view close up on one of its corners (AD, 2003) 30 Fig. 2.7. Polymer based Fabry-Perot sensor (Yoo et al., 1997) 33 Fig. 2.8. Micron sized cantilevers developed at IBM for research purposes (IBM, 2002) 34 Fig. 2.9. Initial steps in fabrication of MEMS and CMOS chips (MEMC, Missouri, 2003) 36 Fig. 2.10. Examples of SUMMiT-V process (SNL, 2002) 38 Fig. 2.11. MEMS chip and its different levels of packaging (SNL, 2001) 40 Fig. 2.12. Operation of different gravimetric sensors (Ivanov, 2000) 42 Fig. 2.13. Comparison of different sized and shaped PZTs available (PI, 2003) 44 Fig. 2.14. Graphical illustration of the operational principle for an AFM where different bend angles reflect the laser beam to different locations on the PSD 45 Fig. 2.15. Cantilever with integrated piezoresistors (Gotszalk et al., 2000) 46 Fig. 2.16. Wheatstone bridge where changes, e.g., in R1, alter the output voltage with a high accuracy and precision 50 Fig. 2.17. Effects of residual stresses (Dartmouth, 2002) 52 12Fig. 2.18. The collapse of a thin cantilever due to stiction 53 Fig. 2.19. A femtocalorimeter utilizing thermal bending effects with piezoresistors (Gotszalk et al., 2000) 55 Fig. 3.1. MikroMasch AFM chip (MM, 2003) 57 Fig. 3.2. Photographs of the AFM chips used in this Thesis 58 Fig. 3.3. Bending moment comparison 62 Fig. 4.1. Graphical representation of ACES methodology and the interdependence of each system towards determining a final solution 66 Fig. 4.2. Idealization and modeling of a cantilever 69 Fig. 4.3. Concentration and pressure relationships based on experimental data of many research studies conducted throughout the years, where the concentration, AR, is in units of atomic ratio (H/Pd) (Lewis, 1967) 74 Fig. 4.4. Permeation rates of hydrogen with circulating or static flows through a membrane of Pd with a high pressure on one side (Lewis, 1967) 76 Fig. 4.5. Experimental data fitted with a line to approximate the amount of volumetric change in palladium due to hydrogen absorption (G. Alefeld and J. Vlkl, 1978) 79 Fig. 4.6. Estimated results from experimental data illustrating the change that occurs in the modulus of elasticity during absorption of hydrogen into palladium (Lewis, 1967) 80 Fig. 4.7. Representation of cantilever with geometric dimensions and a cross-section view through the middle with each of the layers of the cantilever labeled 82 Fig. 4.8. Vibration amplitude as a function of frequency ratio for different values of damping ratio 91 Fig. 4.9. SEM used for measuring thickness of the cantilevers. The samples are inserted into the chamber on the left (botton of the black tube) and the microscope is controlled with the console on the right 96 Fig. 4.10. A comparison of how different resonant frequency response curves affect the width of the bandwidth, which in turn will affect Eq. 4.42 98 13 Fig. 4.11. Single-illumination and single-observation geometry of a fiber optic based OEH system (Furlong and Pryputniewicz, 2000) 100 Fig. 4.12. Optical configuration of the OELIM system (Furlong and Pryputniewicz, 2000) 104 Fig. 4.13. AFM setup utilized 105 Fig. 4.14. A comparison of the PZT used with a MEMS chip attached to the top and a dime 106 Fig. 4.15. The custom made test chamber containing the PZT transducer, which is to be used for testing functional operation of the cantilever-sensing element in a gaseous environment. 108 Fig. 4.16. Laboratory setup for testing functional operation of MEMS chemical sensors; the microscope and test chamber will be contained within a vented hood 108 Fig. 5.1. The microscope setup used in determining the dimensions of the cantilevers 112 Fig. 5.2. Cantilever details and labelling 113 Fig. 5.3. Cantilevers packed in a Gel-Pack as obtained from the manufacturer 114 Fig. 5.4. SEM photographs of the cantilevers, with a progressive close up on the tip 115 Fig. 5.5. SEM photographs of E length cantilevers near the tip from different chips 115 Fig. 5.6. Fundamental resonant frequency of a 1m thick cantilever as a function of active length 118 Fig. 5.7. The percentage overall uncertainty in the cantilever is plotted against the length, while holding the thicknes parameter to 1 m to show the decreasing trend 119 Fig. 5.8. The percentage overall uncertainty in the cantilever is plotted against the thickness, while holding the length parameter to 300 m to show the decreasing trend 119 14Fig. 5.9. Percent contributions by uncertainty of each independent parameter to the overall uncertainty in resonance frequency of the cantilever, as a function of length 120 Fig. 5.10. Percent contributions by uncertainty by each independent parameter to the overall uncertainty in resonance frequency of the cantilever, as a function of thickness 120 Fig. 5.11. Overall uncertainty in the frequency of the cantilever as a function of the uncertainty in the thickness 121 Fig. 5.12. OELIM images of cantilever E32 vibrating under resonant conditions 122 Fig. 5.13. OELIM images of the cantilevers (D, E, and F) of chip 22 vibrating at their fundamental mode of vibration 122 Fig. 5.14. A screen capture of the AFM frequency scan for the cantilever F12, where a resonant frequency peak is identified as 22,160 Hz with the accuracy of 10 Hz 123 Fig. 5.15. Pro/Engineer model and Pro/Mechanica results of the three cantilevers considered 126 Fig. 5.16. Effective mass versus percentage of coating coverage 131 Fig. 5.17. Concentration as a function of time for a cantilever coated with 0.1 m thick layer of palladium 136 Fig. 5.18. A digitally enhanced image of a screen capture from the HP Spectrum analyzer taken while measuring the frequencies of a cantilever 138 Fig. 5.19. The frequency shift in the resonance of a cantilever as a function of concentration of the hydrogen 146 Fig. A.1. A photograph of palladium samples (EC, 2003) 162 Fig. A.2. A sample of titanium, a popular material due to its corrosion resistance (EC, 2003) 163 Fig. A.3. A sample of untreated, unprocessed, uncut silicon (EC, 2003) 163 Fig. B.1. Geometric parameters of a cantilever 166 Fig. D.1. Specifications of the main body of the test chamber 215 15Fig. D.2. Specifications of the lid for the main body of the test chamber 216 Fig. D.3. Fully disassembled view of the chamber showing all parts 216 Fig. D.4. Fully assembled view of chamber with PZT inside 217 Fig. D.5. Isometric view of the assembled chamber 217 Fig. E.1. Schematic of the cantilever and the dimensions used for the data analysis 218 16LIST OF TABLES Table 3.1. Characteristic dimensions, as supplied by the manufacturer, MikroMasch USA, and the corresponding material properties as listed by Madou (2002) 57 Table 4.1. Concentrations of hydrogen in Pd based on experimental data of Fig. 4.3 at 20C with conversion information (Lewis, 1967) 75 Table 5.1. Measured dimensional data and equivalent lengths for all cantilever samples of the D type 115 Table 5.2. Summary of measured dimensions for the cantilevers of the D, E, and F types 116 Table 5.3. Values of material properties of silicon (Madou, 2002) 117 Table 5.4. The uncertainties in each parameter considered 117 Table 5.5. Comparison of the analytical and computational resonance frequency results 125 Table 5.6. Data comparison of analytical, OELIM, and AFM generated frequencies for each cantilever 128 Table 5.7. Properties of materials used 130 Table 5.8. Frequency shift per Hertz-kilogram for distributed and tip loads 133 Table 5.9. Summary of analytically calculated and experimentally determined damping coefficients 139 Table 5.10. The results of the four different cases investigated are compared at 1% hydrogen concentration 144 Table A.1. Physical properties of the materials used 162 Table B.1. Material properties and geometric dimensions used in a sample case to determine sensitivity of the cantilever as a chemical sensor 172 Table C.1. Values of parameters used in uncertainty analysis 184 Table C.2. The uncertainties in each parameter considered 185 17Table C.3. Material properties and thickness of the composite cantilever 191 Table C.4. The uncertainty values used based on current industry practice 197 Table C.5. Values of the two additional independent parameters in the damped case 207 Table E.1. Material properties of silicon used for the analytical calculations 218 Table E.2. Geometrical and resonant frequency data of the cantilever utilized 219 18NOMENCLATURE b width of the cantilever b1 width of the end of cantilever b2 width of point of cantilever bt total width of cantilever c concentration of gas, damping coefficient cc critical damping coefficient co initial concentration of gas cgas concentration of gas at the surface erf Gaussian error function f resonant frequency fi initial resonant frequency f1 initial resonant frequency f2 final resonant frequency f frequency h thickness of the cantilever ht total thickness of the cantilever hTi thickness of the titanium layer hPd thickness of the palladium layer h change of thickness constant k spring constant m effective, or dynamic, mass n effective mass constant nr effective mass constant for a cantilever coverage at value r n(x) effective mass constant in terms or x p pressure ppm parts-per-million r frequency ratio, percent coverage of cantilever with polymer s Sieverts constant, solution to differential equation s1,2 solution to differential equation with two roots t time v velocity vmax maximum velocity of a body wo distributed load x position along the cantilever y displacement y(x) displacement in terms of x ymax maximum displacement z position across the cantilever A amplitude A area of shaped tip of cantilevers C1,2,3,4 constants 19D diffusion rate E modulus of elasticity Eeq equivalent modulus of elasticity ETi modulus of elasticity of titanium EPd modulus of elasticity of palladium ESi modulus of elasticity of silicon Fxmax maximum force acting on a body F force F(y,t) time dependent forcing function FEM finite element method H thickness of damping fluid I moment of inertia K sensitivity vector K1 direction of illumination vector K2 direction of observation vector KE kinetic energy of a system L displacements vector L length of the cantilever Lt total length of the cantilever L1 length of shaped end of cantilever L2 equivalent length of the shaped end of cantilever L3 length of side point of cantilever M mass Mx bending moment along the cantilever MEMS microelectromechanical systems PZT piezoresistive transducer Se sensitivity for an end loaded cantilever Sd sensitivity for a distributed load cantilever Sm mass sensitivity T temperature VPd volume of palladium coating VSi volume of silicon cantilever VTi volume of titanium coating Vt total volume of cantilever with coatings X amplitude X(x) general solution to a differential equation damping ratio phase angle viscosity poissons ratio percentage area coverage by the polymer layer density eq equivalent density Ti density of titanium 20Pd density of palladium Si density of silicon angular frequency n natural angular frequency d damped angular frequency fringe-locus function () matrix ( )T transpose ( ) uncertainty m uncertainty in mass k uncertainty in stiffness f uncertainty in frequency df infinitesimal change in frequency dm infinitesimal change in mass f finite change in frequency m finite change in mass k finite change in stiffness 211. OBJECTIVES The objectives of this thesis were to design, analyze, and perform preliminary characterization of MEMS cantilever-type chemicals sensors for operation at 20C and 1 atm pressure, using ACES methodology. 222. INTRODUCTION To understand the methodology and processes used in this Thesis it is important to have background information on the MEMS cantilever-type chemical sensor. Many different aspects will be covered, some just for completeness, in this chapter. Each section will cover some facet of the multidisciplinary technology of these microsensors, which can be considered to be a part of a broad category of micromachines. 2.1. Micromachine beginnings Ignoring science fiction roots, the impetus towards micromachines was inspired by Prof. Richard P. Feynman of the California Institute of Technology in Pasadena, California in 1959 (Feynman, 1992). In the late 1950s, Prof. Feynman initiated a new area of research that is currently expanding at unprecedented rates. Technology has gone from making macrosized machines to millimeter sized to more recently, within less than two decades, micrometer sized devices as is shown in Fig. 2.1 (Pryputniewicz, 2002a). Figure 2.2 compares a micro gear with a human hair for a visual understanding of their relative scale. While nanotechnology is still largely not of everyday application at the nanometer size, the size of the new microdevices continues to decrease (Pryputniewicz, 2002b). 23Fig. 2.1. A dust mite dwarfs a MEMS device (SNL, 2001). Fig. 2.2. A human hair is compared to a MEMS device (SNL, 2001). The most researched area of micromachines is with microelectromechanical systems (MEMS). These systems are a combination of mechanical and electrical components built into incredibly small devices that are fabricated using sophisticated integrated circuit (IC) batch processing technologies (Pryputniewicz, 2002a). MEMS began in the mid 1980s and some of the first products were accelerometers. MEMS are intricate devices that can have several different moving parts and coupled together with 24other MEMS can sense, analyze and perform complex operations in addition to being able to control and actuate motion on the microscale (Hilbert et al., 2000). MEMS have been labeled one of the most promising and relevant technologies of the 21st century (Hilbert et al., 2000). Revolutionizing industrial and consumer products and processes, their steady infiltration into everyday life has begun to dramatically improve and change the way we live (Madou, 2002). 2.2. MEMS foreword The acronym MEMS is used today to define both the fabrication processes and the devices resulting from these processes (Pryputniewicz and Furlong, 2003). The processes are a result of merging of advanced micromechanical and integrated circuit (IC) technologies. The methods used for making MEMS are similar to those used in the silicon wafer/chip market (Baltes et al., 2002). They are in fact mostly fabricated using silicon wafers or some variation of them. The packaging of MEMS devices also still is, for the most part, based on how chips are made (Pryputniewicz, 2003a). Packaging is a technological barrier that must be worked out, at this time, for each specific application of MEMS. Adaptation of chip fabrication processes allowed development of bulk and surface micromachining and high-aspect ratio micromachining (HARM) (Gormley et al., 2000). Advancements in these fabrication processes allowed construction of three-dimensional devices in the micrometer scale, Fig. 2.3. 25Fig. 2.3. A microchain device: (a) overall view, the white bar at the top left represents 500 m, (b) a close up on the microchain, the white bar at the top left represents 200 m (SNL, 2001). The first true MEMS using current fabrication methods appeared around 1988 as gear trains and tongs (Hsu, 2002). These relatively complex devices were the product of the fabrication techniques such as lithography and etching. Even though the technology for lithography was around for about 200 years, this was the first time it was applied at a micrometer scale. With the continued advancement of the fabrication techniques, more and more complex devices could be made. Todays gears are capable of rotating at one million revolutions per second (Pryputniewicz et al., 2000), a giant leap from the early versions. Beginning with accelerometers and pressure sensors, development of humidity, temperature, and chemical sensors soon followed. In addition to sensors, complex devices such as micro turbines, motors, steam engines, RF switches, optical devices, and actuators have been since made, to name a few (Hilbert et al., 2000). Most of these devices have gone from research to actual commercial products. An example of this occurred in 2002. After about a decade of research, one of the most complex MEMS devices became a commercial product known as the DMD-DLP (Digital Micromirror Device - Digital Light Processing) (TI, 2003). (a) (b) 26The DMD chips are used, e.g., in digital projectors capable of displaying high-resolution images by utilizing and rapidly moving some 1.3 million tiny mirrors characterized by 20 m diagonals, each representing a single pixel, comprising a single MEMS DMD. This MEMS device utilizes a multidisciplinary approach (mechanical, electrical, optical engineering, etc.) simultaneously to produce digital images that are among the best in the world (TI, 2003). Details of construction of the DMD are shown in Fig. 2.4. Fig. 2.4. Details of a DMD chip (TI, 2003): (a) graphic illustration of the construction of the DMD, (b) close up on the DMD array, (c) close up with one mirror removed from the surface, (d) all the mirrors removed from the DMD, (e) close up of a single element of the DMD without mirrors. While it may seem that the only advantage for MEMS are their small size, there are indeed many additional benefits (Madou, 2002). Small sizes imply that less material (b) (c) (a) (d) (e) 27is used and less energy is consumed. Their small size allows for the construction of arrays of hundreds of them on a single chip. Many sensors are also breaking records of sensitivity with some chemical sensors having the capabilities to detect presence of individual molecules and atoms (Britton, Jr., et al., 2000). Perhaps the most prominent advantage to MEMS is the financial factor. By being able to produce thousands of devices on each individual silicon wafer, the cost per unit can be driven down to affordable prices. This can also allow development of disposable devices, which opens entirely new product markets. MEMS devices are rapidly making their way into every aspect of modern life. The future is getting smaller, more accurate, and quicker, and MEMS technology is aiding in the development of NEMS (Nanoelectromechanical Systems) technology (Feynman, 1992; Pryputniewicz, 2002b). NEMS, true nanotechnology, is similar to MEMS only that it deals with devices three orders of magnitude smaller in dimensions, or in the nanometer scale. At this scale, individual molecules can be moved to make devices only a few atoms in dimension. 2.3. Sensors MEMS typically contain the following components: mechanical microstructures, microsensors, microactuators, and microelectronics, Fig. 2.5. These are the general components that make up MEMS as we know them today. 28One of the most common applications of MEMS is by utilizing them as sensors. In fact, the original use for MEMS was as a sensor (Madou, 2002). They have become varied in their applications and can be found almost everywhere in everyday life. The popularity of these sensors is mostly due to the great advantages that they posses. In addition to their small size, MEMS sensors consume very little power and are capable of delivering accurate measurements, which are unparalleled with macro-sized sensors. Fig. 2.5. A schematic illustration of MEMS components and their interdependence (Madou, 2002). Perhaps the most appealing factor to MEMS sensors is that they also are very inexpensive to make. With each wafer producing thousands of sensors affordably, these sensors can now be utilized in areas that were cost prohibitive before with other means. Regardless of application, all MEMS sensors work on the principle of measuring some form of a change, just as any macro sized sensor (Hilbert et al., 2000). Some utilize 29common sensor methods while others take advantage of the benefits of the small scale. While methods may alter, MEMS sensors are able to detect a multitude of different measurable changes. These include, but are not limited to, mechanical, thermal, chemical, radiant, magnetic, and electrical changes. Methods of operation of MEMS sensors are different depending on the use. While there are many methods along with many types of sensors there are a few mechanisms that are common among them. All sensors measure a change and MEMS devices do it with either one or a combination of the following four methods: mechanical, optical, electrical, and/or chemical. These methods are generalizations for the basic system in which a MEMS device gathers information from the surrounding environment. While measurements by MEMS sensors are not limited to these four methods, they are among the most commonly used. It should be noted that each of the methods also depends on electrical interconnections between different components of the MEMS sensor in order for it to function. Sections 2.3.1 through 2.3.4 describe these methods in more detail. 2.3.1. Mechanical detection Certain MEMS sensors measure changes based on mechanical displacement or movement. These are some of the most appealing and intricate of the sensors. This motion is caused, or accented, by relevant external force(s). It can then be detected either externally with optics or internally with electrical electrodes. One of the most common is 30the MEMS accelerometer (Bernstein et al., 1999; Hsu, 2002). A sample accelerometer is shown in Fig. 2.6. In the MEMS accelerometer, Fig. 2.6, there is a proof mass that is suspended on springs and allowed to move in one or two directions, but because of operational requirements of the device, out-of-plane motion is limited. External forces act upon the proof mass causing it to displace. This motion, in turn, changes relative positions between stationary and moving parts of capacitive pickups in the microaccelerometer (represented by long finger-like protrusions in Fig. 2.6), which can be measured electronically as changes in capacitance. These changes in capacitance are interpreted into usable data. Fig. 2.6. A view of the sensor part of a dual-axis accelerometer where the proof mass can be seen with all the perforations in the middle and a quarter-view close up on one of its corners (AD, 2003). Another common type of mechanically actuated sensor is the pressure sensor, which also is one of the oldest type of MEMS sensors. There are several different 31designs for MEMS pressure sensors, but they all can be classified as either one of the following three types: absolute, gage, and differential pressure (Pryputniewicz et al. 2002a; Johari, 2003). Typically, they are constructed as a cavity covered with a diaphragm. When an external pressure is applied to the diaphragm, it deforms, due to stresses produced by applied loads. These stresses are picked up by electrical piezoresistors on the diaphragm, which in turn produce an electrical signal. By measuring this signal and interpreting it, the pressure is determined. 2.3.2. Optical detection While most of the applications for MOEMS (micro-optoelectromechanical systems) are in communications there are a number of uses in other areas (Madou, 2002). Sensing differences in incoming light compared to the outputted light is the basis for optical detection outside of communication purposes. Light is usually passed through, reflected off, or altered by some space containing a medium in question. Some of these sensors are for the detection of chemicals. This shows the multidisciplinary aspect of these devices. Infrared spectrometry is an example of a method that can be used for optical detection (Wang et al., 1999; Wuttig et al., 2002). Another example is the Fabry-Perot optical sensor (Han et al., 1996). Simply said, this sensor works by introducing a gas into a chamber and then passing light through the chamber. Due to changes in the chemical composition the light passing through will be different than without the gas. 32This light is analyzed and from the results a determination can be made about the type of gas being tested. This sensor works similarly to a test tube experiment. 2.3.3. Electrical detection The bulk of all MEMS sensors rely on electrical connections for power and as means of transferring data from the device into the macro world where they can be accessed and analyzed. Piezoresistors are used for the production of electric currents. Piezoresistors are components that convert mechanical energy into electrical and vice versa (MS, 1998). By attaching a piezoresistor to a mechanism that moves, an electrical signal can be produced and measured. From the signal measurements, parameters defining the motion of the body can be calculated. 2.3.4. Chemical detection The detection of chemicals using other chemicals is the basis for the chemical detection method. It is based on calculated and known reactions between certain chemicals. When specific chemicals interact a reaction occurs. Effects of these reactions are known and can be used to determine what chemical caused the reaction. These effects are often exploited by, but not limited to, the use of polymers. In the broad sense, 33a polymer is a laboratory made chemical that can be used for a variety of purposes including controlled, calculated reactions (Lange et al., 2001; Thundat et al., 2000). Polymers for MEMS can be engineered to be sensitive only to specific types of chemicals. When these chemicals are present near the polymer, they get absorbed (or adsorbed) onto it and change one or a few of the properties (e.g., illuminescence, color, opacity, conductivity, resistivity, etc.) of the polymers. This effect is particularly useful and is utilized in several different ways. One way of utilizing this selective polymer sensitivity is with the artificial tongue sensor, or Fabry-Perot sensor (Yoo et al., 1997). Micro storage wells are filled with tiny beads of a polymer designed to attract a specific chemical, as shown in Fig. 2.7, and are illuminated from behind. Fig. 2.7. Polymer based Fabry-Perot sensor: (a) graphic illustration of many wells with different polymers, (b) close up of a well, (c) close up photograph of a Fabry-Perot well (Yoo et al., 1997). The analyte is released over the wells and the polymer beads absorb the selected chemical. Once the polymer absorbs the suspect analyte, there is a chemical reaction that causes a change in color or luminescence in the bead. Because of the illumination from behind, it is possible to detect these shifts in color. This change is detected with a CCD (a) (b) (c) 34(Charge Coupled Device) array, and means that the sensor identified the suspect chemical. Since each well is very small, it is possible to make many wells, each with a different polymer designed for detecting a different chemical. By having many wells filled with different polymers it is possible to measure complex analytes quickly as well as being able to detect multiple analytes simultaneously. A far more robust method of using polymers, however, is with cantilever sensing elements. Cantilever elements also rely on polymer absorption, but utilize it differently. A micron sized cantilever, Fig. 2.8, is coated with the polymer of choice that is then exposed to the suspect analyte (Ilic et al., 2000, 2001). Fig. 2.8. Micron sized cantilevers developed at IBM for research purposes (IBM, 2002). When the polymer absorbs the desired chemical, it changes the mass of the cantilever, which in turn changes the fundamental frequency of the cantilever. With the cantilever attached to piezoresistors, this change can then be measured and the analyte can be identified. 352.4. MEMS cantilever chemical sensor Detection of chemicals is required for many industries. MEMS sensors bring a novel solution for that requirement in small devices with high sensitivity. One aspect of these sensors is that they be customized for most applications. While they seem like the perfect solution, they are not without flaws. There are many aspects to chemical sensors. They can be simple or highly complex. In most cases, they are very accurate. It is possible, for example, for a cantilever sensor to detect mass changes in picograms (Madou, 2002). Many different technologies must come together in order to allow development of MEMS (Pryputniewicz, 2002b). Sections 2.4.1 through 2.4.16 discuss some of the aspects of MEMS cantilever chemical sensor technology and while not all of them will pertain directly to this Thesis they were inserted for completeness and future work. 2.4.1. Material The most commonly used material for fabricating MEMS devices is silicon and silicon compounds (Madou, 2002). Although there are many other materials that can also be used, the overall properties of silicon are very good at small scales (Petersen, 1982; Pryputniewicz, 2002b). Combination of the yield strength and modulus of elasticity (among other properties) of silicon is especially useful for the life and durability of small devices. Some other materials, such as silicon carbide or diamond, offer better properties 36but they also cost many times more than silicon making them uneconomical except for the most demanding and specific purposes (Thaysen et al., 2002). For the fabrication of MEMS and CMOS (complementary metal-oxide semiconductor) chips alike, silicon is first processed into ingots of about 4 feet in length. Ingots are bars of silicon (or of any other chip materials) with 99.99% purity. Purity is essential to making consistent flawless devices. The ingots are specially made with the crystallographic orientation of the silicon arranged into either [100] or [111] as these are the most commonly used (Madou, 2002). Silicon ingots and wafers are shown in Fig. 2.9 during fabrication and after slicing. Fig. 2.9. Initial steps in fabrication of MEMS and CMOS chips: (a) preparation of a silicon for slicing, (b) visual inspection of silicon wafers (MEMC, 2003). This crystallographic orientation of silicon allows MEMS to have well defined and sharp edges and shapes with perpendicular angles resulting from etching as described (a) (b) 37in Section 2.4.2. The ingots are then sliced into wafers usually ranging from 100 mm to 300 mm in diameter and about 1 mm in thickness. These wafers act as the substrate or foundation for all MEMS devices. 2.4.2. Fabrication The fabrication of MEMS is an entire field of research on its own (Pryputniewicz, 2002a). It is extremely diverse and still expanding (Madou, 2002). With the growing number of companies that fabricate MEMS, the technology is continually improving. With each breakthrough, MEMS technology becomes more affordable, better, and easier to fabricate in ever increasing quantities. The technology is also pushing the size envelope and continually working towards the development of smaller devices such as NEMS (Madou, 2002). There are several different processes for producing MEMS; most prominent of these, however, is lithography. Lithography currently produces the smallest and most exact features available in MEMS (Pryputniewicz, 2003a). It is comparable to dry etching, which has many different sub-methods. Surface micromachining and dry etching have an advantage over other techniques such as bulk micromachining and LIGA (German acronym for x-ray lithography, electrodeposition, and molding) in the size of the features made. MEMS devices are fabricated in a manner similar to making a cake. Layers are deposited on one another, patterned, and the components are released to form 3D 38structures. Depending on the process, the number of layers, and the complexity of the design, the fabrication process can take up to a few months. MEMS devices are fabricated on wafers made usually of silicon, as described in Section 2.4.1. Using one of the processes available, a MEMS is built up one layer at a time. In most cases, the layer is grown in a vapor filled environment. This growth can be time consuming, but there have been improvements in it. These improvements include automation of the steps. This automation has allowed the price of MEMS to be reduced while improving quality of the devices that are produced. One place of such improvement is at Sandia National Laboratories (SNL) in Albuquerque, New Mexico (SNL, 2001). SNL has developed an automated process known as SUMMiT-V (Sandias Ultra-planar MEMS Multilevel Technology), which is capable of fabricating MEMS devices out of up to 5 structural layers in complexity, Fig. 2.10 (Pryputniewicz, 2002a). SNL is the only place in the world, at this time, capable of fabricating a 5 layer MEMS using a sacrificial surface micromachining process. The rest of the world is limited to 3 layer fabrication processes. Fig. 2.10. Examples of SUMMiT-V process: (a) a rachet gear, (b) a clutch activated transmission, (c) a thermally actuated motor, (d) example of the 5 structural layers of deposition (SNL, 2001). (a) (b) (c) 39While it may seem simple, the fabrication of each layer is quite complex. Using lithography, for example, a series of steps are required for each layer (Hsu, 2002; Madou, 2002; Pryputniewicz, 2002a). Starting with the deposition of the first structural polysilicon layer onto the wafer, the polysilicon is exposed to masks, light, and chemicals to etch holes and/or dimples. The next layer is sacrificial and it is also exposed to masks, light, and chemicals to continue with the fabrication. Masks somewhat resemble cookie cutters cutting out shapes in a layer of cookie dough. The light used is to harden, or soften, certain parts of a given layer so that when they are exposed to specific chemicals, they will etch away leaving the desired shapes. The deposition and etching steps alternate between the structural polysilicon layers and the sacrificial SiO2 layers making shapes in each layer and continuing until the desired device is fabricated. 2.4.3. Packaging One of the biggest hurdles in MEMS technology is with packaging, Fig. 2.11. The development of MEMS devices has come a long way and new boundaries have been reached (Pryputniewicz, 1986; Pryputniewicz et.al., 2001a; Hsu, 2002; Pryputniewicz, 2002b; 2003). A problem still remains however in being able to communicate efficiently with these devices. While MEMS are small, the packages that contain them are still considerable in size to facilitate their handling and assembly. Packaging of MEMS is application specific. It is too vast for the scope of this Thesis and will not be addressed herein. 40Fig. 2.11. MEMS chip and its different levels of packaging (SNL, 2001): (a) MEMS component, (b) MEMS component contained sealed chip, (c) Macro sized chip containing the MEMS component 2.4.4. Sensing types and methods As discussed in Sections 2.3.1 through 2.3.4, MEMS sensors are used for a variety of purposes. Most of the different types of chemical sensors available are targeted for the detection of specific chemicals. These can either be regular, simple composition chemicals or complex biological agents. Chemical sensors have even been used for the detection of DNA strands with different base sequences (Fritz et al., 2000a) and are also capable of measuring pH values in analytes (Bashir et al., 2002). With so many different types of sensors, it is necessary to categorize the chemical detection methods to better understand MEMS. Of the different types of sensors, there are two basic methods of operation to detect chemicals. The two methods could be called contact and non-contact methods. (c) (a) (b) 41Contact methods include any system in which the analyte is physically touched or altered. In non-contact mode, the analyte is observed from a distance and its behavior and/or characteristics are measured and studied. The contact method uses polymers deposited on cantilever sensing elements to absorb the specific chemicals and thus produce a change in mass, stress, electrical, or thermal properties of the element. For a change in mass, an increase in mass can be detected by measuring the change in frequency of the resonating cantilever, which has mass as one of its parameters, Fig. 2.12. The second way is by measuring change in stress in the element, which occurs when mass is absorbed into the polymer. This stress (or surface stress) change can then be measured by piezoresistors on the surface of the sensing element. The other way to detect the suspect chemical is to use a thermocouple to measure the change in temperature due to heat produced from the absorption of the analyte by the polymer. All of these methods have been successfully incorporated into chemical sensors (Koll et al., 1999; Lang et al., 1999; Jensenius et al., 2000; Kerness et al., 2000; Thaysen et al., 2001; Subramanian et al., 2002; Baselt et al., 2003). Two other methods of interest are SAW (Surface Acoustic Wave) and BAW (Bulk Acoustic Wave) sensors, Fig. 2.12 (Ivanov, 2000). They function when the analyte comes in contact with the polymer containing either a surface or a bulk wave that travels through the material. A change in these waves can be measured with sensors. The downside of these methods is that they have a much lower accuracy when compared with the methods described earlier in this section. 42Fig. 2.12. Operation of different gravimetric sensors: (a) graphic illustration of a cantilever bending under the load of analytes attaching to the polymer coating, (b) illustration behind the principles of BAW and SAW (Ivanov, 2000). 2.4.5. Resonance frequency The main principle behind cantilever sensors is with the measuring of change in the resonance frequency (Betts et al., 2000). Resonance frequency is the frequency that produces the largest amplitude that a vibrating body can achieve. This frequency is dependent on the spring constant of the body and its dynamic mass. Should one or the other of these parameters change, the resonant frequency will also change. This is the basis behind using change in resonance frequency as means of detection. Alteration in the mass of a vibrating cantilever changes the total mass, which in turn shifts the resonant frequency to some other frequency. By measuring this shift, one can determine the amount of mass that was added to the cantilever assuming the spring constant remained constant. If the cantilever is selective to the kind of analyte it absorbs, then in addition to detecting the chemical, if present, the sensor can also detect the concentration of it. This method of chemical detection is highly accurate. Some experiments have successfully (a) (b) 43demonstrated the ability to detect a change in mass of as little as 0.7 picograms (Madou, 2002). 2.4.6. Cantilever actuation methods In order for the MEMS sensor to function in dynamic, or frequency, response mode, it is necessary for the cantilever to be vibrating (Arecco and Pryputniewicz, 2003). There are several ways of actuating a cantilever into its resonance frequency, which can be classified as direct and indirect actuation methods (Cho and Ahn, 2002; Li et al., 2002; Stephan et al., 2002). Indirect methods include using acoustic vibration to shake the sensor much like a piece of paper in front of a speaker vibrates from the compression waves in the air. Another indirect method has a magnetic coat deposited on the cantilever and an inductance coil underneath it to generate magnetic fields that attract and repel the cantilever into vibration. For direct methods, the most common is by the use of a PZT (Pb-Zr-Ti transducer or, as it is sometimes called, piezoelectric z-axis actuating transducer), which is capable of vibrating at different frequencies very accurately depending on the driving voltage (Harley, 2002; Mehta et al., 2001; Turner and Zhang, 2001). Another method that is less common, but of great interest, is by fabricating piezoresistive electrodes directly on the cantilever and bending it rapidly into vibration by bimorph actuation. Figure 2.13 shows some sample PZTs available commercially. 44Fig. 2.13. Comparison of different sizes and shapes of PZTs available: (a) Open-Loop LVPZT Translators, (b) Preloaded Open- & Closed-Loop High-Load HVPZT Translators, (c) PICMA Chip Monolithic Multilayer Piezo Actuators (PI, 2003). It is of interest to combine and use direct and indirect methods when conducting research. It is even possible to combine both direct methods into one package that allows for high-speed functionality (Kim et al., 2003). 2.4.7. Cantilever I/O In order for the sensor to function it is necessary to have some means of communicating out the data it collects. There are several ways of accomplishing this and the most relevant to this Thesis will be discussed. (b) (a) (c) 45One of the most common ways of detecting frequency shifts is by optically inspecting the cantilever. Using a technique similar to those in AFMs (atomic force microscopes), a laser is reflected off the cantilever tip and into a position sensitive diode (PSD) (Raiteri et al., 2001; Kim et al., 2001a; Battiston et al., 2001). The reflected angle changes due to the bending of the cantilever. This change is detected by the PSD and it can be measured and analyzed, Fig. 2.14. While the technique is proven and quite reliable, it is too bulky for portable purposes. Fig. 2.14. Graphical illustration of the operational principle for an AFM where different bend angles reflect the laser beam to different locations on the PSD. Another method that is also quite popular is by using piezoresistive elements on the cantilever itself (Porter et al., 2003; Lange et al., 2002). Any changes in the surface stress due to bending will cause an electrical output from the piezoresistive electrodes. 46This output can be analyzed and the frequency can be calculated. Figure 2.15 shows how a typical piezoresistor is used on a cantilever. Fig. 2.15. Cantilever with integrated piezoresistors: (a) view of piezoresistive cantilever used for scanning probe microscopy (SPM) applications, (b) close up of the Wheatstone bridge piezoresistive elements (Gotszalk et al., 2000). 2.4.8. Polymer thickness In fabricating cantilever sensors with special coatings it is important to take into account the thickness of this layer. The thickness affects several aspects of the sensor. Mostly, it affects the rate at which the polymer becomes saturated with the suspect analyte (Britton et al., 2000). The thicker the polymer, the longer it takes for the polymer to reach saturation. This is not really a problem unless time is a critical factor in the function of the sensor. It is generally preferred to make the coating as thin as possible, however, to minimize other effects. One of the most prominent of these effects is that of residual stresses which will be discussed in Section A thicker layer of polymer (a) (b) 47is more reactive to effects of different coefficients of thermal expansion and also it is more sensitive to pressure effects, which are discussed in Sections 2.4.16. 2.4.10. Ficks law of diffusion For any particle that is absorbed into another there is a rate at which the diffusion takes place (Hughes and Bastasz, 1988; Streeter et al., 1998; Hu, 2001). During diffusion, or mass transport, a gas (or liquid) with a higher concentration of a sample will move into an area of lesser concentration. Diffusion is usually an interstitial motion in that the sample gas travels to the voids in the lattice structure of a material. The diffusion can be modeled with Ficks laws. These laws address two specific conditions, steady state and nonlinear (non-steady) diffusion. During steady state diffusion (first law), the rate is constant, as it would be in an ideal condition with infinite volumes of gas passing through a membrane. The nonlinear state (second law) is a more accurate model for finite volume cases where the diffusion rate will change as the concentration gradient equalizes. 2.4.11. Reference cantilevers In order to guarantee sensor functionality and reliability, the data quality must be high. Because of signal noise from a variety of different sources, such as thermal effects, 48electrical noise, and others, it is important to include a parallel system to improve the signal. By incorporating a reference cantilever it is possible to minimize noise and improve the signal quality. Reference cantilevers function by having them react to only the noise producing effects, which means that they are not coated with the analyte-attracting polymer (Thaysen et al., 2000). The signals of the reference and coated cantilevers can then be compared and external forces (noise) can be removed computationally thus improving quality of data. 2.4.12. Quality factor Most vibrating mechanical structures have a variable known as the quality factor, Q (or Q-value) (Yasumura et al., 2000). This value is a non-dimensional parameter for quantitatively assessing the quality of a structure. In other words, the Q-value is a measure of how much energy is dissipated in a vibrating system. The higher the Q-value, the better the structure is capable to conserve energy. If the structure can conserve energy efficiently, the structure can operate longer and produce more data. It should be noted that Q has a great dependence on damping and thus the environment that the structure operates in. Structures in air usually operate much better than those in liquids and the Q will be the highest in a vacuum environment (Tamayo et al., 2001). The quality factor is reflected in the shape of a resonance frequency peak. The narrower the peak of the frequency response curve, the higher the Q value is while the opposite is also true. The width of the peak is also sometimes referred to as the 49bandwidth of the resonance frequency curve. This bandwidth is measured at half power points, though, for consistency and convenience. 2.4.13. Electrical aspects Practically all MEMS devices collect measurements electronically. This means that there must be a mechanism for producing a signal to the macro world. The signal produced must be directly linked to the motion, or action, that the MEMS performs in order to determine quantitative results. This mechanical to electrical effect is accomplished with a phenomenon usually referred to as a piezoresistive effect (MS, 1998). The piezoresistive effect is the change in electrical resistivity that occurs with the application of mechanical stress, which allows conversion of stress into proportional, measurable electrical signals. The piezoresistive effect is observed in piezoresistive elements and materials. One of the most common gauges used is a piezoresistive strain gauge (Chow et al., 2002; Thaysen et al., 2002). They are used for detecting the action, or motion, that is incurred in a sensor. The gauge is usually attached to, or built into, the device as that will produce the most accurate results. The operation is based upon measuring increasing stress/strain in an element of the MEMS. When a strain is produced in the gauge, an electric signal is generated. This signal can then be amplified and measured. 502.4.13.1. Wheatstone bridge The basic and most commonly used method of gathering and measuring the electric signal generated from gauges is with a Wheatstone bridge circuit, Fig. 2.16. The Wheatstone bridge is a classic and common configuration for determining voltage differences in electrical circuits. Four resistors are connected together with a middle resistor, R1, (usually the strain gauge or other measuring gauge) acting as a gate switch. Any variation in current in the middle resistor will cause a difference in voltage output from the circuit (Pauw, 1958). For MEMS applications, the middle resistor would be a piezoresistive element, which can change its resistance with mechanical changes. Fig. 2.16. Wheatstone bridge where changes, e.g., in R1, alter the output voltage with a high accuracy and precision. Feed back loop Because the method of extracting data is usually electrical, there is typically noise present in the results. This noise and errors in the data can be reduced, however, with the 51use of feed back loops (Abadal et al., 2001; Humphris et al., 2000; Sulcheck et al., 2000). Feed back loops is a post processing technique used to increase accuracy of the data in which the data are refined several times. It is utilized with computers and special circuit setups. Active feed back loops continuously correct the data being processed so that error is minimized. These can be very complex systems and require a lot of knowledge in electrical engineering. Because of time, equipment, and complexity concerns, feed back loops will not be incorporated into the experiments planned for this Thesis. 2.4.14. Inherent problems In designing a MEMS device, there are a multitude of variables that must be accounted for. With so many variables, there are inherent problems that arise (Handel, 2001; Manias et al., 2001; Kassegne et al., 2002). Tweaking and adjusting of techniques and design can overcome most, however. There are two problems, though, that are reoccurring and of some concern: residual fabrication stresses and stiction. Residual stresses During the fabrication of a multi-layered MEMS device two or more dissimilar layers are deposited on top of one another depending on the process and design. Because of differences in coefficients of thermal expansion (CTE) of the materials, residual 52stresses will be produced in these layers due to the fabrication processes (Fritz et al., 2000b; Lu et al., 2001; Pryputniewicz et al., 2002c, 2003b). A lot of the fabrication techniques involve depositing layers at high temperatures (600C +). When the fabrication is over and temperatures return to room temperature (~20C), different materials will shrink (or expand) by different amounts leading to stress gradients (Pryputniewicz, 2003a, 2003b). These gradients will manifest themselves as warping of structures, cracks, or other failure modes; Fig. 2.17 shows the effects of residual stresses on a MEMS component. This is a constant design problem that must be taken into account. One method of reducing and/or eliminating residual stresses is by annealing the device after the deposition process. Anneling is a process of heating the device at a certain temperature for a specific time in order to allow for any stress to dissipate. This is a proven process, but requires a lot of experimental verification and takes up valuable production time. Fig. 2.17. Effects of residual stresses: (a) a MEMS device is bent after its release, (b) a close up on the device showing how extreme the bending is (Dartmouth, 2002). (a) (b) 532.4.14.2. Stiction A very common problem that plagues devices with small thickness (1 to 4 m) is a phenomenon known as stiction. Stiction is probably the most serious of problems that occur in micromachining (Buks and Roukes, 2001). Stiction occurs when two flat surfaces are near each other, Fig. 2.18. This close proximity along with forces such as van der Waals cause the components to tend to stick to one another. Fig. 2.18. Collapse of a thin cantilever due to stiction. In a cantilever type sensor, stiction can cripple a device making it useless. It is possible to fix stiction, but during that process excessive forces are required which can damage or destroy the device. Another major problem with stiction is that it can occur at any time and not just during fabrication. There are solutions for this problem. New organic modifiers (coatings) have been developed against stiction and have been tested and shown to lessen the effect (Kim et al., 2001b). With some limited solutions already in place and continued research, stiction will hopefully become a problem of the past in the near future. 542.4.15. Effects of temperature Temperature has a critical effect on the functionality of the MEMS sensors (Hanson et al., 2001; Pryputniewicz, 2003a). Because the cantilever sensor is based on a bimaterial ( e.g., substrate and polymer coating) there will be two materials and thus two coefficients of thermal expansion. Any temperature change in the environment will affect the beam (Gotszalk et al., 2000). Increase or decrease in the temperature will produce a surface stress on the cantilever due to different CTEs and thus create a pronounced bending as shown by Stoney (1909) and Pryputniewicz et al. (2003a). This bending can alter results of any detection system setup. If any temperature change will be expected it is necessary to include reference beams to allow for the compensation of this phenomenon (Hanson et al., 2001). For producing better results and data it would be desirable to reduce the temperature induced bending, as this would lessen the amount of compensation required from the reference beams. This means that the temperature of the sample should ideally remain as constant as possible. The effects of temperature have been, however, capitalized to create calorimeters with femto-range accuracy, Fig. 2.19 (Kerness et al., 2000). 2.4.16. Effects of pressure The pressure of the environment in which the sensor is in can affect the results produced. If the pressure of the environment increases, the dynamic viscosity of the damping fluid increases. This increase in damping reduces the Q-value and can also 55reduce the deflection of the cantilever which both, in turn, decreases the data quality and reliability. In addition to changing the damping, an increase in pressure will also alter the concentration of the gas in the environment, which can affect the absorption rate, and equilibrium giving shifted results. Fig. 2.19. A femtocalorimeter utilizing thermal bending effects with piezoresistors (Gotszalk et al., 2000). 563. MEMS SAMPLES This chapter describes the MEMS samples used in this Thesis and their characteristic parameters and includes information for the proposed MEMS chemical sensor. 3.1. MikroMasch USA cantilevers Sample cantilevers used in this Thesis were purchased from MikroMasch USA (MM, 2003). The company is headquartered in Estonia, Spain, and their main product are atomic force microscope (AFM) chips. AFM chips are silicon-based cantilevers that have a high aspect ratio tip at the end. They are used for topographic surveying on the nanometer scale of the samples investigated. The samples used in this Thesis are actual AFM contact silicon cantilevers that are uncoated and also are without any tips, model number CSC12 (MM, 2003). They are micromachined cantilevers with the fixed ends being integral parts of a chip. It is important that the cantilevers be uncoated so that a special coating can be deposited on them without any other underlying layer. Figure 3.1 shows an AFM chip with cantilevers with tips from MikroMasch, while Fig. 3.2 shows the special samples purchased for this Thesis. The nomenclature illustration in Fig. 3.1 applies to the samples used in this Thesis. In fact, the labeling of the cantilevers (A-F) shall be used from here on to refer to each cantilever, as only the three longest cantilevers (D-F) shall be investigated in this Thesis. Table 3.1 lists the manufacturers specifications of the samples purchased. As can be seen from the photographs, the chips 57contain 6 cantilevers of varying lengths, all listed in Table 3.1. The thickness and width of each of the cantilevers should be the same according to manufacturers specifications. The cantilevers and the chips are all made of single-crystal silicon with the dimensions and material properties listed in Table 3.1. Fig. 3.1. MikroMasch AFM chip: (a) SEM photograph of cantilevers (A,B,C) with tips, (b) dimensions of the chip and cantilevers as well as a standardized nomenclature (MM, 2003). Table 3.1. Characteristic dimensions, as supplied by the manufacturer, MikroMasch USA, and the corresponding material properties as listed by Madou (2002). A B CCharacteristics Min Typical Max Min Typical Max Min Typical MaxLength, L 5 m 110 90 130Width, b 3 m 35 35 35Thickness, h 0.3 m 0.7 1 1.3 0.7 1 1.3 0.7 1 1.3Resonance freq., f kHz 65 105 150 95 155 230 50 75 105Force constant, k N/m0.25 0.95 2.5 0.45 1.75 5 0.15 0.6 1.5D E FCharacteristics Min Typical Max Min Typical Max Min Typical MaxLength, L 5 m 300 350 250Width, b 3 m 35 35 35Thickness, h 0.3 m 0.7 1 1.3 0.7 1 1.3 0.7 1 1.3Resonance freq., f kHz 9.5 14 19 7 10 14 14 20 28Force constant, k N/m0.01 0.05 0.1 0.01 0.03 0.08 0.02 0.08 0.2190 Modulus of elasticity, E 3 GPa Density, 0.05 g/cm32.33 (a) (b) 58Fig. 3.2. Photographs of the AFM chips used in this Thesis: (a) a view of the entire chip from above with all the cantilevers visible. The cantilevers of interest are the ones on the right, (b) a top view of the primary cantilevers used in this Thesis (D,E,F), (b) a photograph of the underside of the cantilevers (D,E,F). 3.2. Chemicals used This Thesis is based around a sensor that is capable of detecting a specific chemical. Thus it is important to select a chemical that is easy to detect and useful for research purposes. While it is possible to detect just about any analyte with the right polymer, some polymers are easier to make and are more accessible than others. Due to the focus of this Thesis and time/cost issues, availability played an important role in determining the polymer/analyte combination to use. In the process of coating, there are (a) (b) (c) 59many different types of materials that are fairly standard and can be found and used easily and quickly. Most are not used for a gravimetric chemical sensor, but there are a few that are. One of these materials is palladium (Pd). Palladium will, at room temperature, absorb 800-900 times its own volume of hydrogen (Lewis, 1967). This is a significant amount for a very reasonable condition. The process is also reversible. The palladium-hydrogen system is very well documented and very easy to conduct experiments with (Lewis, 1967). While there are other absorption systems, they are much more complex and can be very difficult to set up and maintain. Most systems are not reversible in absorption without some kind of treatment to the absorbing material. 3.2.1. Palladium The element palladium (Pd) was selected as the absorbent layer for the cantilever because of its sensitivity to hydrogen (Darling, 1958). As mentioned in Section 3.2, it has an ability to absorb (not adsorb) great quantities of hydrogen. It is in fact a very unique ability in that there are few other materials that naturally behave as palladium does with hydrogen. Because of this phenomenon, palladium is often used as a filter of hydrogen as when it is heated; only hydrogen will diffuse through it. A true benefit of this interaction for the Thesis is that the absorption will occur at room temperature. The hydrogen then desorbs out of the palladium when the hydrogen source is removed. These are highly desired features that simplify the experiments. Properties of palladium are found in Appendix A. 603.2.2. Hydrogen The element hydrogen (H2) is the lightest and most abundant in the universe. It is a very versatile element and a very promising fuel. When it burns the only by-product is water. The one fact about hydrogen that limits its use is its explosive nature. Hydrogen is explosive from 4% - 40% concentration. This means that if the concentration is below or above that range, hydrogen will not burn. Because of this range, great care must be taken when setting up any experiment dealing in hydrogen to prevent a concentration greater than 4%. Pre-mixing a nitrogen-hydrogen gas with 1% and less hydrogen concentration reduces the risk factor. This would be acceptable since this Thesis will investigate a sensor for detecting the lower concentration limits (